A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study

Anticipating future migration trends is instrumental to the development of effective policies to manage the challenges and opportunities that arise from population movements. However, anticipation is challenging. Migration is a complex system, with multifaceted drivers, such as demographic structure, economic disparities, political instability, and climate change. Measurements encompass inherent uncertainties, and the majority of migration theories are either under-specified or hardly actionable. Moreover, approaches for forecasting generally target specific migration flows, and this poses challenges for generalisation. In this paper, we present the results of a case study to predict Irregular Border Crossings (IBCs) through the Central Mediterranean Route and Asylum requests in Italy. We applied a set of Machine Learning techniques in combination with a suite of traditional data to forecast migration flows. We then applied an ensemble modelling approach for aggregating the results of the different Machine Learning models to improve the modelling prediction capacity. Our results show the potential of this modelling architecture in producing forecasts of IBCs and Asylum requests over 6 months. The explained variance of our models through a validation set is as high as 80%. This study offers a robust basis for the construction of timely forecasts. In the discussion, we offer a comment on how this approach could benefit migration management in the European Union at various levels of policy making.

BOSCO Claudio;  MINORA Umberto;  ROSINSKA Anna;  TEOBALDELLI Maurizio;  BELMONTE Martina; 

2025-01-10

CAMBRIDGE UNIVERSITY PRESS

JRC136548

2632-3249 (online),   

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/45652144691DA4EA1BEC80BFA39BAFDC/S2632324924000488a.pdf/a-machine-learning-architecture-to-forecast-irregular-border-crossings-and-asylum-requests-for-policy-support-in-europe-a-case-study.pdf,    https://publications.jrc.ec.europa.eu/repository/handle/JRC136548,   

10.1017/dap.2024.48 (online),    https://doi.org/10.1017/dap.2024.48,